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<a href="_v_d_c_m_a_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * \brief       Implements the VD-CMA-ES Algorithm</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \author     Oswin Krause</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \date        April 2014</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * </span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * </span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> *</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> */</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span> </div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_DIRECT_SEARCH_VD_CMA_H</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="preprocessor">#define SHARK_ALGORITHMS_DIRECT_SEARCH_VD_CMA_H</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span> </div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_single_objective_optimizer_8h.html">shark/Algorithms/AbstractSingleObjectiveOptimizer.h</a>&gt;</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="preprocessor">#include &lt;<a class="code" href="_multi_variate_normal_distribution_8h.html">shark/Statistics/Distributions/MultiVariateNormalDistribution.h</a>&gt;</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#include &lt;<a class="code" href="_individual_8h.html">shark/Algorithms/DirectSearch/Individual.h</a>&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;<a class="code" href="_penalizing_evaluator_8h.html">shark/Algorithms/DirectSearch/Operators/Evaluation/PenalizingEvaluator.h</a>&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#include &lt;<a class="code" href="_elitist_selection_8h.html">shark/Algorithms/DirectSearch/Operators/Selection/ElitistSelection.h</a>&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment"></span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment">/// \brief Implements the VD-CMA-ES Algorithm</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">///</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">/// The VD-CMA-ES implements a restricted form of the CMA-ES where the covariance matrix is restriced to be (D+vv^T)</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">/// where D is a diagonal matrix and v a single vector. Therefore this variant is capable of large-scale optimisation</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">///</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">/// For more reference, see the paper</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// Akimoto, Y., A. Auger, and N. Hansen (2014). Comparison-Based Natural Gradient Optimization in High Dimension. </span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// To appear in Genetic and Evolutionary Computation Conference (GECCO 2014), Proceedings, ACM</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">///</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// The implementation differs from the paper to be closer to the reference implementation and to have better numerical</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// accuracy.</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// \ingroup singledirect</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment"></span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="foldopen" id="foldopen00052" data-start="{" data-end="};">
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html">   52</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_v_d_c_m_a.html">VDCMA</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>{</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    <span class="keywordtype">double</span> chi( std::size_t n ) {</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>        <span class="keywordflow">return</span>( std::sqrt( <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>( n ) )*(1. - 1./(4.*n) + 1./(21.*n*n)) );</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>    }</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment"></span> </div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">    /// \brief Default c&#39;tor.</span></div>
<div class="foldopen" id="foldopen00061" data-start="{" data-end="}">
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a1b75875028d4bcc325723dde79d2d449">   61</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a1b75875028d4bcc325723dde79d2d449" title="Default c&#39;tor.">VDCMA</a>(random::rng_type&amp; rng = <a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>):m_initialSigma(0.0), mpe_rng(&amp;rng){</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        <a class="code hl_variable" href="classshark_1_1_abstract_optimizer.html#a72daf583d406e144b90869f311baa594">m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0af46b9e1111a0858df3670fe12e4ffbf0">REQUIRES_VALUE</a>;</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    }</div>
</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    <span class="comment"></span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00066" data-start="{" data-end="}">
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a802231164288dca08bf2d504c4a62733">   66</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a802231164288dca08bf2d504c4a62733" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;VDCMA-ES&quot;</span>; }</div>
</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment"></span> </div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">    /// \brief Calculates lambda for the supplied dimensionality n.</span></div>
<div class="foldopen" id="foldopen00070" data-start="{" data-end="}">
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#aa745975f4a9c3349eaf9a2ebbc2a876a">   70</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aa745975f4a9c3349eaf9a2ebbc2a876a" title="Calculates lambda for the supplied dimensionality n.">suggestLambda</a>( std::size_t dimension ) {</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        <span class="keywordflow">return</span> unsigned( 4. + ::floor( 3. * ::log( <span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>( dimension ) ) ) ); <span class="comment">// eq. (44)</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    }</div>
</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment"></span> </div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment">    /// \brief Calculates mu for the supplied lambda and the recombination strategy.</span></div>
<div class="foldopen" id="foldopen00075" data-start="{" data-end="}">
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a3d4aaa6f933f6658d22d046ecc0023ee">   75</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a3d4aaa6f933f6658d22d046ecc0023ee" title="Calculates mu for the supplied lambda and the recombination strategy.">suggestMu</a>( std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5" title="Returns a immutable reference to the size of the offspring population .">lambda</a>) {</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5" title="Returns a immutable reference to the size of the offspring population .">lambda</a> / 2; <span class="comment">// eq. (44)</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    }</div>
</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span> </div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;<a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a387173a404de5babcadf81f812154442" title="initializes the optimizer using a predefined starting point">::init</a>;</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    </div>
<div class="foldopen" id="foldopen00081" data-start="{" data-end="}">
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a387173a404de5babcadf81f812154442">   81</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a387173a404de5babcadf81f812154442" title="initializes the optimizer using a predefined starting point">init</a>( <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; p) {</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        <a class="code hl_function" href="classshark_1_1_abstract_optimizer.html#ae7a23300641448c761b6aa0305b7ef66" title="Convenience function that checks whether the features of the supplied objective function match with t...">checkFeatures</a>(function);</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        </div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5" title="Returns a immutable reference to the size of the offspring population .">lambda</a> = <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aa745975f4a9c3349eaf9a2ebbc2a876a" title="Calculates lambda for the supplied dimensionality n.">suggestLambda</a>( p.size() );</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aca9a92a95cdf91f4346826ee9f565f14" title="Returns the size of the parent population .">mu</a> = <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a3d4aaa6f933f6658d22d046ecc0023ee" title="Calculates mu for the supplied lambda and the recombination strategy.">suggestMu</a>(  <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5" title="Returns a immutable reference to the size of the offspring population .">lambda</a> );</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#ac1319db547ccbf4d5d4fa9cd7b3487f3" title="Accesses the current step size.">sigma</a> = m_initialSigma;</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        <span class="keywordflow">if</span>(m_initialSigma == 0) </div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#ac1319db547ccbf4d5d4fa9cd7b3487f3" title="Accesses the current step size.">sigma</a> = 1.0/std::sqrt(<span class="keywordtype">double</span>(p.size()));</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        </div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a387173a404de5babcadf81f812154442" title="initializes the optimizer using a predefined starting point">init</a>( function,</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>            p,</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>            <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5" title="Returns a immutable reference to the size of the offspring population .">lambda</a>,</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>            <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aca9a92a95cdf91f4346826ee9f565f14" title="Returns the size of the parent population .">mu</a>,</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>            <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#ac1319db547ccbf4d5d4fa9cd7b3487f3" title="Accesses the current step size.">sigma</a></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        );</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    }</div>
</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment"></span> </div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    /// \brief Initializes the algorithm for the supplied objective function.</span></div>
<div class="foldopen" id="foldopen00099" data-start="{" data-end="}">
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a0fa083a499437065ddcee88bcdafbfe3">   99</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a0fa083a499437065ddcee88bcdafbfe3" title="Initializes the algorithm for the supplied objective function.">init</a>( </div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function, </div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; initialSearchPoint,</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5" title="Returns a immutable reference to the size of the offspring population .">lambda</a>, </div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aca9a92a95cdf91f4346826ee9f565f14" title="Returns the size of the parent population .">mu</a>,</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        <span class="keywordtype">double</span> initialSigma</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>    ) {</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span> </div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        m_numberOfVariables = function.numberOfVariables();</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        m_lambda = <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5" title="Returns a immutable reference to the size of the offspring population .">lambda</a>;</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        m_mu = <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aca9a92a95cdf91f4346826ee9f565f14" title="Returns the size of the parent population .">mu</a>;</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        m_sigma = initialSigma;</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span> </div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        m_mean = blas::repeat(0.0,m_numberOfVariables);</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        m_vn.resize(m_numberOfVariables);</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != m_numberOfVariables;++i){</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>            m_vn(i) = <a class="code hl_function" href="namespaceshark_1_1random.html#a18f302ea18f70835c59935973ba8ea84" title="Draws a number uniformly in [lower,upper] by drawing random numbers from rng.">random::uni</a>(*mpe_rng,0,1.0/m_numberOfVariables);</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        }</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        m_normv = norm_2(m_vn);</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        m_vn /= m_normv;</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        </div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        m_D = blas::repeat(1.0,m_numberOfVariables);</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        m_evolutionPathC = blas::repeat(0.0,m_numberOfVariables);</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        m_evolutionPathSigma = blas::repeat(0.0,m_numberOfVariables);</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>        </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        <span class="comment">//set initial point</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        m_mean = initialSearchPoint;</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point = initialSearchPoint;</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value = function(initialSearchPoint);</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        </div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        m_counter = 0;<span class="comment">//first iteration</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>            </div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        <span class="comment">//weighting of the mu-best individuals</span></div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        m_weights.resize(m_mu);</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        <span class="keywordflow">for</span> (std::size_t i = 0; i &lt; m_mu; i++){</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>            m_weights(i) = ::log(<a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aca9a92a95cdf91f4346826ee9f565f14" title="Returns the size of the parent population .">mu</a> + 0.5) - ::log(1. + i);</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        }</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        m_weights /= sum(m_weights);</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        </div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="comment">// constants based on (4) and Step 3 in the algorithm</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        m_muEff = 1. / sum(<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(m_weights)); <span class="comment">// equal to sum(m_weights)^2 / sum(sqr(m_weights))</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        m_cSigma = 0.5/(1+std::sqrt(m_numberOfVariables/m_muEff));</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        m_dSigma = 1. + 2. * std::max(0., std::sqrt((m_muEff-1.)/(m_numberOfVariables+1)) - 1.) + m_cSigma;</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span> </div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        m_cC = (4. + m_muEff / m_numberOfVariables) / (m_numberOfVariables + 4. +  2 * m_muEff / m_numberOfVariables);</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="keywordtype">double</span> correction = (m_numberOfVariables - 5.0)/6.0;</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>        m_c1 = correction*2 / (<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(m_numberOfVariables + 1.3) + m_muEff);</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        m_cMu = std::min(1. - m_c1, correction* 2 * (m_muEff - 2. + 1./m_muEff) / (<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(m_numberOfVariables + 2) + m_muEff));</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>    }</div>
</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span><span class="comment"></span> </div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span><span class="comment">    /// \brief Executes one iteration of the algorithm.</span></div>
<div class="foldopen" id="foldopen00150" data-start="{" data-end="}">
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a9df8d807bdf57909ac6b5b5a984faf03">  150</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a9df8d807bdf57909ac6b5b5a984faf03" title="Executes one iteration of the algorithm.">step</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; function){</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_individual.html" title="Individual is a simple templated class modelling an individual that acts as a candidate solution in a...">Individual&lt;RealVector, double, RealVector&gt;</a> <a class="code hl_typedef" href="_t_s_p_8cpp.html#aa2af735cda68526094633853c8a9894d">IndividualType</a>;</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        std::vector&lt; IndividualType &gt; offspring( m_lambda );</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span> </div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        <a class="code hl_struct" href="structshark_1_1_penalizing_evaluator.html" title="Penalizing evaluator for scalar objective functions.">PenalizingEvaluator</a> penalizingEvaluator;</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        <span class="keywordflow">for</span>( std::size_t i = 0; i &lt; offspring.size(); i++ ) {</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>            createSample(offspring[i].<a class="code hl_function" href="namespaceshark.html#a68954303294e98c77d03dad52e32bd9e">searchPoint</a>(),offspring[i].chromosome());</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        }</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        penalizingEvaluator( function, offspring.begin(), offspring.end() );</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span> </div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>        <span class="comment">// Selection</span></div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        std::vector&lt; IndividualType &gt; parents( m_mu );</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        <a class="code hl_struct" href="structshark_1_1_elitist_selection.html" title="Survival selection to find the next parent set.">ElitistSelection&lt;IndividualType::FitnessOrdering&gt;</a> selection;</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        selection(offspring.begin(),offspring.end(),parents.begin(), parents.end());</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        <span class="comment">// Strategy parameter update</span></div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        m_counter++; <span class="comment">// increase generation counter</span></div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        updateStrategyParameters( parents );</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span> </div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point= parents[ 0 ].searchPoint();</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value= parents[ 0 ].unpenalizedFitness();</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>    }</div>
</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span><span class="comment"></span> </div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span><span class="comment">    /// \brief Accesses the current step size.</span></div>
<div class="foldopen" id="foldopen00173" data-start="{" data-end="}">
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#ac1319db547ccbf4d5d4fa9cd7b3487f3">  173</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#ac1319db547ccbf4d5d4fa9cd7b3487f3" title="Accesses the current step size.">sigma</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordflow">return</span> m_sigma;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>    }</div>
</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment"></span> </div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">    /// \brief Accesses the current step size.</span></div>
<div class="foldopen" id="foldopen00178" data-start="{" data-end="}">
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#abd76b0216a4b252eb3ef5eb40ad7ae14">  178</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#abd76b0216a4b252eb3ef5eb40ad7ae14" title="Accesses the current step size.">setSigma</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#ac1319db547ccbf4d5d4fa9cd7b3487f3" title="Accesses the current step size.">sigma</a>) {</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        m_sigma = <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#ac1319db547ccbf4d5d4fa9cd7b3487f3" title="Accesses the current step size.">sigma</a>;</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>    }</div>
</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>    <span class="comment"></span></div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span><span class="comment">    /// \brief set the initial step size of the algorithm. </span></div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span><span class="comment">    /// Sets the initial sigma at init to a given value. If this is 0, which it is</span></div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span><span class="comment">    /// by default, the default initialisation will be sigma= 1/sqrt(N) where N </span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="comment">    /// is the number of variables to optimize.</span></div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span><span class="comment">    /// this method is the prefered one instead of init()</span></div>
<div class="foldopen" id="foldopen00189" data-start="{" data-end="}">
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a52d9ab529c1c8dc9fd7b2b3f81e026df">  189</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a52d9ab529c1c8dc9fd7b2b3f81e026df" title="set the initial step size of the algorithm.">setInitialSigma</a>(<span class="keywordtype">double</span> initialSigma){</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>        m_initialSigma = initialSigma;</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>    }</div>
</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span> </div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span><span class="comment"></span> </div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span><span class="comment">    /// \brief Accesses the current population mean.</span></div>
<div class="foldopen" id="foldopen00195" data-start="{" data-end="}">
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a046fe17ddb1b6e2991bb89235b7c1ae0">  195</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a046fe17ddb1b6e2991bb89235b7c1ae0" title="Accesses the current population mean.">mean</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        <span class="keywordflow">return</span> m_mean;</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>    }</div>
</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span><span class="comment"></span> </div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span><span class="comment">    /// \brief Accesses the current weighting vector.</span></div>
<div class="foldopen" id="foldopen00200" data-start="{" data-end="}">
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#aa899fb6ab6bf117a8ed37cd058dd2434">  200</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aa899fb6ab6bf117a8ed37cd058dd2434" title="Accesses the current weighting vector.">weights</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        <span class="keywordflow">return</span> m_weights;</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>    }</div>
</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span><span class="comment"></span> </div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span><span class="comment">    /// \brief Accesses the evolution path for the covariance matrix update.</span></div>
<div class="foldopen" id="foldopen00205" data-start="{" data-end="}">
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a0dc57927069dfe99f09c788e6e6a1377">  205</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a0dc57927069dfe99f09c788e6e6a1377" title="Accesses the evolution path for the covariance matrix update.">evolutionPath</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>        <span class="keywordflow">return</span> m_evolutionPathC;</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>    }</div>
</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span><span class="comment"></span> </div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span><span class="comment">    /// \brief Accesses the evolution path for the step size update.</span></div>
<div class="foldopen" id="foldopen00210" data-start="{" data-end="}">
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a7e045148cdb4d8f928104da38a31be86">  210</a></span><span class="comment"></span>    RealVector <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a7e045148cdb4d8f928104da38a31be86" title="Accesses the evolution path for the step size update.">evolutionPathSigma</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        <span class="keywordflow">return</span> m_evolutionPathSigma;</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>    }</div>
</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>    <span class="comment"></span></div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span><span class="comment">    ///\brief Returns the size of the parent population \f$\mu\f$.</span></div>
<div class="foldopen" id="foldopen00215" data-start="{" data-end="}">
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#aca9a92a95cdf91f4346826ee9f565f14">  215</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aca9a92a95cdf91f4346826ee9f565f14" title="Returns the size of the parent population .">mu</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>        <span class="keywordflow">return</span> m_mu;</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>    }</div>
</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>    <span class="comment"></span></div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span><span class="comment">    ///\brief Returns a mutabl reference to the size of the parent population \f$\mu\f$.</span></div>
<div class="foldopen" id="foldopen00220" data-start="{" data-end="}">
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#ac8e9c6a657a7c2c8b68721d9d207b75e">  220</a></span><span class="comment"></span>    std::size_t&amp; <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#ac8e9c6a657a7c2c8b68721d9d207b75e" title="Returns a mutabl reference to the size of the parent population .">mu</a>(){</div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>        <span class="keywordflow">return</span> m_mu;</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>    }</div>
</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>    <span class="comment"></span></div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span><span class="comment">    ///\brief Returns a immutable reference to the size of the offspring population \f$\mu\f$.</span></div>
<div class="foldopen" id="foldopen00225" data-start="{" data-end="}">
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5">  225</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#a69f5bc9d17bfbbe896e8925586bdbba5" title="Returns a immutable reference to the size of the offspring population .">lambda</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>        <span class="keywordflow">return</span> m_lambda;</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>    }</div>
</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span><span class="comment"></span> </div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span><span class="comment">    ///\brief Returns a mutable reference to the size of the offspring population \f$\mu\f$.</span></div>
<div class="foldopen" id="foldopen00230" data-start="{" data-end="}">
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno"><a class="line" href="classshark_1_1_v_d_c_m_a.html#af8784176f99b23cbc1d941d5da30bf0b">  230</a></span><span class="comment"></span>    std::size_t &amp; <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#af8784176f99b23cbc1d941d5da30bf0b" title="Returns a mutable reference to the size of the offspring population .">lambda</a>(){</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>        <span class="keywordflow">return</span> m_lambda;</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>    }</div>
</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span> </div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span><span class="keyword">private</span>:<span class="comment"></span></div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span><span class="comment">    /// \brief Updates the strategy parameters based on the supplied offspring population.</span></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span><span class="comment">    /// The chromosome stores the y-vector that is the step from the mean in D=1, sigma=1 space.</span></div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span><span class="comment"></span>    <span class="keywordtype">void</span> updateStrategyParameters( std::vector&lt;<a class="code hl_class" href="classshark_1_1_individual.html" title="Individual is a simple templated class modelling an individual that acts as a candidate solution in a...">Individual&lt;RealVector, double, RealVector&gt;</a> &gt;&amp; offspring ) {</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>        RealVector m( m_numberOfVariables, 0. );</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>        RealVector z( m_numberOfVariables, 0. );</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        </div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        <span class="keywordflow">for</span>( std::size_t j = 0; j &lt; offspring.size(); j++ ){</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>            noalias(m) += m_weights( j ) * offspring[j].searchPoint();</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>            noalias(z) += m_weights( j ) * offspring[j].chromosome();</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>        }</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        <span class="comment">//compute z from y= (1+(sqrt(1+||v||^2)-1)v_n v_n^T)z</span></div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        <span class="comment">//therefore z= (1+(1/sqrt(1+||v||^2)-1)v_n v_n^T)y</span></div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>        <span class="keywordtype">double</span> b=(1/std::sqrt(1+<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(m_normv))-1);</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        noalias(z)+= b*inner_prod(z,m_vn)*m_vn;</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        </div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        <span class="comment">//update paths</span></div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>        noalias(m_evolutionPathSigma) = (1. - m_cSigma)*m_evolutionPathSigma + std::sqrt( m_cSigma * (2. - m_cSigma) * m_muEff ) * z;</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        <span class="comment">// compute h_sigma</span></div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>        <span class="keywordtype">double</span> hSigLHS = norm_2( m_evolutionPathSigma ) / std::sqrt(1. - pow((1 - m_cSigma), 2.*(m_counter+1)));</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        <span class="keywordtype">double</span> hSigRHS = (1.4 + 2 / (m_numberOfVariables+1.)) * chi( m_numberOfVariables );</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        <span class="keywordtype">double</span> hSig = 0;</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        <span class="keywordflow">if</span>(hSigLHS &lt; hSigRHS) hSig = 1.;</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        noalias(m_evolutionPathC) = (1. - m_cC ) * m_evolutionPathC + hSig * std::sqrt( m_cC * (2. - m_cC) * m_muEff ) * (m - m_mean) / m_sigma;</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>        </div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        </div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        </div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        <span class="comment">//we split the computation of s and t in the paper in two parts</span></div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        <span class="comment">//we compute the first two steps and then compute the weighted mean over samples and</span></div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        <span class="comment">//evolution path. afterwards we compute the rest using the mean result</span></div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        <span class="comment">//the paper describes this as first computing S and T for all samples and compute the weighted</span></div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        <span class="comment">//mean of that, but the reference implementation does it the other way to prevent numerical instabilities</span></div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>        RealVector meanS(m_numberOfVariables,0.0);</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>        RealVector meanT(m_numberOfVariables,0.0);</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        <span class="keywordflow">for</span>(std::size_t j = 0; j != <a class="code hl_function" href="classshark_1_1_v_d_c_m_a.html#aca9a92a95cdf91f4346826ee9f565f14" title="Returns the size of the parent population .">mu</a>(); ++j){</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>            computeSAndTFirst(offspring[j].chromosome(),meanS,meanT,m_cMu*m_weights(j));</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>        }</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>        computeSAndTFirst(m_evolutionPathC/m_D,meanS,meanT,hSig*m_c1);</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        </div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>        <span class="comment">//compute the remaining mean S and T steps</span></div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>        computeSAndTSecond(meanS,meanT);</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>        </div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>        <span class="comment">//compute update to v and d</span></div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>        noalias(m_D) += m_D*meanS;</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>        noalias(m_vn) = m_vn*m_normv+meanT/m_normv;<span class="comment">//result is v and not vn</span></div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>        <span class="comment">//store the new v separately as vn and its norm</span></div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>        m_normv = norm_2(m_vn);</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span>        m_vn /= m_normv;</div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>        </div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>        <span class="comment">//update step length</span></div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>        m_sigma *= std::exp( (m_cSigma / m_dSigma) * (norm_2(m_evolutionPathSigma)/ chi( m_numberOfVariables ) - 1.) ); <span class="comment">// eq. (39)</span></div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>        </div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>        <span class="comment">//update mean</span></div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>        m_mean = m;</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>    }</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>    </div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>    <span class="comment">//samples a point and stores additionally y=(x-m_mean)/(sigma*D)</span></div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>    <span class="comment">//as this is required for calculation later</span></div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>    <span class="keywordtype">void</span> createSample(RealVector&amp; x,RealVector&amp; y)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>        x.resize(m_numberOfVariables);</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>        y.resize(m_numberOfVariables);</div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != m_numberOfVariables; ++i){</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span>            y(i) = <a class="code hl_function" href="namespaceshark_1_1random.html#a972c5f7f031612a130aa077fc9136a9f" title="Draws a number from the normal distribution with given mean and variance by drawing random numbers fr...">random::gauss</a>(*mpe_rng,0,1);</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>        }</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>        <span class="keywordtype">double</span> a = std::sqrt(1+<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(m_normv))-1;</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>        a *= inner_prod(y,m_vn);</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>        noalias(y) +=a*m_vn;</div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>        noalias(x) = m_mean+ m_sigma*m_D*y;</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>    }</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>    <span class="comment"></span></div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span><span class="comment">    ///\brief computes the sample wise first two steps of S and T of theorem 3.6 in the paper</span></div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span><span class="comment">    /// S and T arguments accordingly</span></div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span><span class="comment"></span>    <span class="keywordtype">void</span> computeSAndTFirst(RealVector <span class="keyword">const</span>&amp; y, RealVector&amp; s,RealVector&amp; t, <span class="keywordtype">double</span> weight )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>        <span class="keywordflow">if</span>(weight == 0) <span class="keywordflow">return</span>;<span class="comment">//nothing to do</span></div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>        <span class="keywordtype">double</span> yvn = inner_prod(y,m_vn);</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>        <span class="keywordtype">double</span> normv2 = <a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(m_normv);</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>        <span class="keywordtype">double</span> gammav = 1+normv2;</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>        <span class="comment">//step 1</span></div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>        noalias(s) += weight*(<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(y) - (normv2/gammav*yvn)*(y*m_vn)- 1.0);</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>        <span class="comment">//step 2</span></div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>        noalias(t) += weight*(yvn*y - 0.5*(<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(yvn)+gammav)*m_vn);</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>    }</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>        <span class="comment"></span></div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span><span class="comment">    ///\brief computes the last three steps of S and T of theorem 3.6 in the paper</span></div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span><span class="comment"></span>    <span class="keywordtype">void</span> computeSAndTSecond(RealVector&amp; s,RealVector&amp; t)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>        RealVector vn2 = m_vn*m_vn;</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>        <span class="keywordtype">double</span> normv2 = <a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(m_normv);</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>        <span class="keywordtype">double</span> gammav = 1+normv2;</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>        <span class="comment">//alpha of 3.5</span></div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>        <span class="keywordtype">double</span> alpha = <a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(normv2)+(2*gammav - std::sqrt(gammav))/max(vn2);</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>        alpha=std::sqrt(alpha);</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span>        alpha /= 2+normv2;</div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>        alpha = std::min(alpha,1.0);</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>        <span class="comment">//constants (b,A) of 3.4</span></div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>        <span class="keywordtype">double</span> b=-(1-<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(alpha))*<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(normv2)/gammav+2*<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(alpha);</div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>        RealVector A= 2.0 - (b+2*<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(alpha))*vn2;</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>        RealVector invAvn2= vn2/A;</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span>        </div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span>        <span class="comment">//step 3</span></div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>        noalias(s) -= alpha/gammav*((2+normv2)*(m_vn*t)-normv2*inner_prod(m_vn,t)*vn2);</div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span>        <span class="comment">//step 4</span></div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span>        noalias(s) = s/A -b*inner_prod(s,invAvn2)/(1+b*inner_prod(vn2,invAvn2))*invAvn2;</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span>        <span class="comment">//step 5</span></div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span>        noalias(t) -= alpha*((2+normv2)*(m_vn*s)-inner_prod(s,vn2)*m_vn);</div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>    }</div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>    </div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span>    std::size_t m_numberOfVariables; <span class="comment">///&lt; Stores the dimensionality of the search space.</span></div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span>    std::size_t m_mu; <span class="comment">///&lt; The size of the parent population.</span></div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span>    std::size_t m_lambda; <span class="comment">///&lt; The size of the offspring population, needs to be larger than mu.</span></div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span> </div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span>    <span class="keywordtype">double</span> m_initialSigma;<span class="comment">///0 by default which indicates initial sigma = 1/sqrt(N)</span></div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span>    <span class="keywordtype">double</span> m_sigma;</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>    <span class="keywordtype">double</span> m_cC; </div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>    <span class="keywordtype">double</span> m_c1; </div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>    <span class="keywordtype">double</span> m_cMu; </div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span>    <span class="keywordtype">double</span> m_cSigma;</div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>    <span class="keywordtype">double</span> m_dSigma;</div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>    <span class="keywordtype">double</span> m_muEff;</div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span> </div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span>    RealVector m_mean;</div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span>    RealVector m_weights;</div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span> </div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span>    RealVector m_evolutionPathC;</div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno">  359</span>    RealVector m_evolutionPathSigma;</div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span>    <span class="comment"></span></div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span><span class="comment">    ///\brief normalised vector v </span></div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span><span class="comment"></span>    RealVector m_vn;<span class="comment"></span></div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span><span class="comment">    ///\brief norm of the vector v, therefore  v=m_vn*m_normv</span></div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span><span class="comment"></span>    <span class="keywordtype">double</span> m_normv;</div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span>    </div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno">  366</span>    RealVector m_D;</div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span> </div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span>    <span class="keywordtype">unsigned</span> m_counter; <span class="comment">///&lt; counter for generations</span></div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span>    </div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span>    random::rng_type* mpe_rng;</div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>    </div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span>    </div>
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno">  373</span>};</div>
</div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span> </div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>}</div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span> </div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span><span class="preprocessor">#endif</span></div>
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